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Copyright © 2020 Zhou, Zhou, Zhang, Tang, Tang, Shi, Yang, Zhou, Liu, Yu, Liu, Tao and Lu.Background As the sixth most common cancer of worldwide, head and neck cancers (HNC) are springing from oral cavity, pharynx and larynx and there is no strong biomarker for prognosis. Rates of 5 years survival with HNC remain relatively low in decades with improvement of treatments. Evidence that single nucleotide polymorphisms (SNPs) play a part in cancer prognosis is growing. Methods We conducted an exome-wide association study among 261 patients with head and neck squamous cell carcinoma (HNSCC) and then validated in The Cancer Genome Atlas (TCGA) database for survival by using the Cox proportional hazards regression models and Kaplan-Meier analyses. Results After combining the result of the two stages, 4 SNPs were significantly associated with HNSCC survival (rs16879870 at 6q14.3 adjusted HR = 2.02, 95%CI = 1.50-2.73, P = 3.88 × 10-6; rs2641256 at 17p13.2 adjusted HR = 0.67, 95%CI = 0.56-0.80, P = 7.51 × 10-6; rs2761591 at 11p13 adjusted HR = 2.07, 95%CI = 1.50-2.87, P = 1.16 × 10-5; and rs854936 at 22q11.21 adjusted HR = 1.92, 95%CI = 1.43-2.57, P = 1.27 × 10-5). Besides, we constructed a receiver operating characteristic (ROC) model to estimate predictive effect of the novel SNPs combined with clinical stage in HNSCC prognosis (AUC = 0.715). We also found the genotype of rs16879870 and rs854936 was significantly associated with the expression of gene GJB7 (P = 0.013) and RTN4R (P = 0.047) in cancer tissues of TCGA, respectively. Conclusion Our findings suggested that the SNPs (rs16879870, rs2641256, rs2761591, rs854936) might play a crucial role in prognosis of HNSCC. Copyright © 2020 He, Ji, Li, Wang, Ma and Yuan.Objectives To predict the anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients non-invasively with machine learning models that combine clinical, conventional CT and radiomic features. Methods This retrospective study included 335 lung adenocarcinoma patients who were randomly divided into a primary cohort (268 patients; 90 ALK-rearranged; and 178 ALK wild-type) and a test cohort (67 patients; 22 ALK-rearranged; and 45 ALK wild-type). One thousand two hundred and eighteen quantitative radiomic features were extracted from the semi-automatically delineated volume of interest (VOI) of the entire tumor using both the original and the pre-processed non-enhanced CT images. Twelve conventional CT features and seven clinical features were also collected. Normalized features were selected using a sequential of the F-test-based method, the density-based spatial clustering of applications with noise (DBSCAN) method, and the recursive feature elimination (RFE) method. Selected features were then usr study demonstrates that radiomics-derived machine learning models can potentially serve as a non-invasive tool to identify ALK mutation of lung adenocarcinoma. Copyright © 2020 Song, Zhu, Mao, Li, Han, Du, Wu, Song and Jin.[This corrects the article DOI 10.3389/fonc.2019.01430.]. Copyright © 2020 Lemos, Oliveira, Martins, de Azevedo, Rodrigues, Ketzer and Rumjanek.Background Although international guidelines recommend bone screening for premenopausal breast cancer patients taking adjuvant tamoxifen, the effects of tamoxifen on osteoporosis and related risks remain controversial. The objective of this study was to investigate the incidence of and risk factors for osteoporosis and osteoporotic fractures in younger breast cancer patients. Methods A nationwide retrospective cohort study was conducted using South Korea Health Insurance Review and Assessment Service claims data. The rates of osteoporosis and osteoporotic fracture were calculated as incident cases per person-year and disease-free probability rates were analyzed with the Kaplan-Meier method. To identify risk factors for osteoporosis and osteoporotic fracture, a multivariable Cox proportional hazard regression model was applied. Results From January 2009 to December 2014, a total of 47,649 breast cancer patients were included. The incidence rates of osteoporosis and osteoporotic fracture were 23.59 and 2.40 peryright © 2020 Lee, Alqudaihi, Kang, Kim, Lee, Ko, Son, Ahn, Lee, Han, Kim, Hur, Lee and Chung.Background To assess the role of nodal involvement in stage III renal cell carcinoma (RCC) according to the American Joint Committee on Cancer (AJCC) 8th staging system. We compared the survival outcomes of RCC patients with pT1-3N1M0 disease and those with pT3N0M0 or stage IV (stratified as pT4NanyM0 and pTanyNanyM1) disease in a large population-based cohort. Methods A cohort of 3,112 eligible patients with RCC was identified from the Surveillance, Epidemiology, and End Results (SEER) database, registered between January 2004 and December 2015. Kaplan-Meier and Cox proportional hazards models were used to evaluate the overall survival (OS), and cancer-specific survival (CSS). The prognostic value of the modified stage for pT1-3N1M0 disease was assessed by nomogram-based analyses. Propensity score matching (PSM) was used to adjust for potential baseline confounding. Results Patients with pT1-3N1M0 disease showed similar survival outcomes (median OS 41.0 vs. 38.0 months, P = 0.77; CSS 45.0 vs. 39.0 months, P = 0.59) to pT4NanyM0 patients, whereas the significantly better survival outcome was found for pT3N0M0 patients. After PSM, comparable survival rates were observed between pT1-3N1M0 group and pT4NanyM0 group, which were still significantly worse than the survival of pT3N0M0 patients. this website The modified stage IIIA (pT3N0M0), IIIB (pT1-3N1M0, pT4NanyM0), and IV (pTanyNanyM1) showed higher predictive accuracy than AJCC stage system in the nomogram-based analyses (concordance index 0.70 vs. 0.68, P less then 0.001 for OS; 0.71 vs. 0.69, P less then 0.001 for CSS). Conclusions The pT1-3N1M0 RCC might be reclassified as stage IIIB together with pT4NanyM0 disease for better prediction of prognosis, further examination and validation are warranted. Copyright © 2020 Han, Li, Li, Wang, Zhang, Qiao, Song and Fu.Quality assured pathology services are integral to provision of optimal management for patients with head and neck cancer. Pathology services vary globally and are dependent on resources in terms of both laboratory provision and availability of a highly trained and accredited workforce. Ensuring a high-quality pathology service depends largely on close working and effective communication between the clinical team providing treatment and the pathologists providing laboratory input. Laboratory services should be quality assured by achieving external accreditation, most often by conforming to International Organization for Standardization (ISO) standards such as ISO15189 sometimes with ISO17025 or alternatively ISO17020. Quality of diagnostic reporting can be assured by the ISO but clinical teams should endeavor to work with pathologists who engage in continuing professional development, external quality assurance and audit. Research also contributes to diagnostic reporting quality. A number of initiatives in the UK such as the EPSRC/MRC funded Molecular Pathology Nodes and the National Cancer Research Institute Cellular-Molecular Pathology initiative (C-M Path), for example, have linked pathologists, industry and researchers.